Robust LLM Extractor for HTML/Markdown in TypeScript
While working with LLMs for structured web data extraction, we saw issues with invalid JSON and broken links in the output. This led me to build a library focused on robust extraction and enrichment: - Clean HTML conversion: transforms HTML into LLM-friendly markdown with an option to extract just the main content - LLM structured output: Uses Gemini 2.5 flash or GPT-4o mini to balance accuracy and cost. Can also also use custom prompt - JSON sanitization: If the LLM structured output fails or doesn't fully match your schema, a sanitization process attempts to recover and fix the data,…
In plain words
Robust LLM Extractor for HTML/Markdown is a TypeScript library that extracts structured data from web pages using large language models. It converts HTML to markdown, sends it to Gemini 2.5 Flash or GPT-4o Mini for processing, and includes tools to fix malformed JSON output, validate extracted URLs, and handle relative links. The library is designed for developers who need reliable data extraction from web content with built-in error recovery for incomplete or invalid results.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
While working with LLMs for structured web data extraction, we saw issues with invalid JSON and broken links in the output. This led me to build a library focused on robust extraction and enrichment: - Clean HTML conversion: transforms HTML into LLM-friendly markdown with an option to extract just the main content - LLM structured output: Uses Gemini 2.5 flash or GPT-4o mini to balance accuracy and cost. Can also also use custom prompt - JSON sanitization: If the LLM structured output fails or doesn't fully match your schema, a sanitization process attempts to recover and fix the data, especially useful for deeply nested objects and arrays - URL validation: all extracted URLs are validated - handling relative URLs, removing invalid ones, and repairing markdown-escaped links I'd love to hear if anyone else has experimented with LLMs for data extraction or if you have any questions about this approach!
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